Prospectus Idol Entry

Paper Covers Rock: Why Pitchers Don't Control Batting Average on Balls in Play

Voros McCracken wrote on Baseball Prospectus over eight years ago to release one of the most controversial findings in the history of sabermetric thought: he said that pitchers only control whether a hitter strikes out, walks, hits a homerun, or hits the ball in play, but have little to no control over whether a ball in play is a hit or an out. Many have argued that the reason this is true is because walks, strikeouts, and homeruns are outcomes that have nothing to do with defense. Anything else has the potential to land on the field or be caught beforehand, and so it is the defense's job to cover ground effectively. The pitcher had to keep the ball from being hit out of the park, or from being hit altogether.

In that article, he states that pitchers control three main things: strikeouts, walks, and homeruns, and that pitchers who were above average in recording strikeouts, avoiding walks, and avoiding homeruns one year were frequently above average in those categories the following year. However, the pitchers who were above average at preventing high Batting Average on Balls in Play (BABIP) one year were no more likely to be above average at preventing high BABIP the following year than pitchers who were below average.

McCracken argued that the hitters the pitcher faced and the defense behind him were the key determinants in a pitcher's BABIP. Comparatively, a batter's BABIP not only shows a much higher year to year correlation, but you can also learn a lot through breaking it down bybattedballtype.

Correlation coefficients are a measure of how well two variables move together. These year-to-year correlations test whether pitchers' strikeout rates, for example, tend to be high if they were high the previous year (a positive correlation), low if they were high the previous year (a negative correlation), or no more likely to be high or low if they were high the previous year (zero correlation). Correlation coefficients take on values between -1 and 1, where a correlation of 1 means that the two move together in perfect unison, 0 means that they are unrelated, and -1 means that the two always move in opposite directions. Look at the following table stating the year-to-year correlations for each of the following statistics using 2003-2008 data on the pitchers who threw at least 100 innings in consecutive seasons.

As you can see, pitchers do seem to have some persistence in their BABIP, but a large portion of that correlation is actually the defense behind him that keeps the whole team's BABIP low or high (as shown by the fact that the BABIP correlation is smaller when you only consider the difference between the pitcher's BABIP and that of all pitchers on his team). Hitters seem to control their BABIP a lot more. Check out the same table for hitters who had 300 plate appearances or more in consecutive seasons from 2003-2008.

HITTERS' STATISTIC CORRELATION WITH SAME STATISTIC
THE FOLLOWING YEAR
Strikeouts per Plate Appearance .8467
Unintentional walks per Plate Appearance .7751
Homeruns per At-Bat .7420
BABIP .3657

The following two diagrams illustrate this point pretty well. The first graph shows pitcher's strikeout rates one year versus the next year. As you can see, the pitchers who had low strikeout rates one year had low strikeout rates the next year.

This graph shows the difference between a pitcher's BABIP and his team's overall BABIP allowed in one year compared to the same difference the next year. It does not seem like being vulnerable to hits on balls in play one year makes you any more or less likely to be vulnerable to the same thing the next year.

If you try to explain this concept to the average baseball fan, you'll get some funny looks. After all, it goes against all common understanding of the game. How could a pitcher not really affect his BABIP? Pitchers who have good stuff must be able to induce weak contact, right? It goes against logic, and while we can look at the numbers and see it is true, you are likely to get a response like "oh, you can manipulate numbers to tell you anything." When you go to a ballgame and you see the visiting team blast a line drive into the gap, you blame the pitcher, don't you? This is where the problem lies. If he left a meatball over the plate, it seems like it is probably his fault.

McCracken himself has since softened his stance and most sabermetricians in the know generally believe that pitchers have some control over BABIP, but very little. They certainly do not seem to have any special ability to avoid line drives-the correlation in my sample between line drive rates one year and the next is 0.00! In fact, old BABIP data is nearly irrelevant in predicting their performance because it is correlated with the other factors, like strikeout and homerun rates, which matter more.

Groundball rate is somewhat correlated with BABIP (positively) only because flyballs that are not homeruns are more likely to be turned into outs than groundballs. The correlation is not all that high (.11), and is barely far enough from zero to assume there is a real correlation. There is also a slight negative correlation (-.18) between a pitcher's strikeout rate and his BABIP the following year, too. In reality, the question is why BABIP has such a poor correlation for pitchers.

To project future hitter performance, not only is it useful to know his homerun, walk, and strikeout rates, but it is important to know his BABIP. However, to project future pitcher performance, it is still useful to look at his homerun, walk, and strikeout rates, but knowing his BABIP will not help the projection at all. When projection systems like PECOTA make ERA projections that turn out to be accurate, even though they do not seem in line with the previous year's ERA, they are using a trick-they are mostly using strikeouts, walks, and homeruns to predict ERA, and are not using last year's ERA much at all! Of course, the pitcher does seem to have a little tendency to control BABIP, but that effect is captured by looking at his strikeouts and groundball rate. If hitters had a high BABIP against him the year before, that might be an accident. It's best to look at how many people he struck out and that will tell you whether he can induce weak contact better than how much weak contact he caused last year.

There is clearly a dilemma here. On one hand, we see a meatball lined into the gap, and we know the pitcher probably messed up. On the other hand, we keep looking at all these numbers that tell us pitchers who give up a lot of line drives are no more likely than other pitchers to give up a lot of line drives the next year. What gives?

The concept is no more complicated than rock/paper/scissors. If you have ever played rock/paper/scissors before, you know that you can't keep doing the same thing over and over, and you can't be any more likely to do rock, paper, or scissors at any given time. You need to randomize. In game theory, we call this approach a mixed strategy.

When it comes to pitching, we know that if a pitcher threw a fastball on the outside corner every single pitch all year long, the hitters would eventually discover this and start hitting the ball the other way. The result would be a lot of opposite field hits and a high BABIP. Pitchers who are predictable get told by their coaches to "keep the hitter honest." What they are saying in game theory terms is "play a mixed strategy." Even if you are a little predictable one day, you can switch your approach. The result is that sometimes the pitcher throws an 0-2 fastball right down the middle and the hitter is caught looking. Pitchers are taught to randomize their pitches and locations, and as a result, a line drive often comes from a hitter guessing right. Chipper Jonesrecently said, "For me, plate discipline is being able to know what pitch you want to put in play before you step in the box and not swinging at anything else but that." That is certainly one way to guess right, and that's why Chipper Jones' BABIP has been .343, .352, and .388 compared to league averages of around .300 in the last three years. For pitchers, the best way to have a low BABIP is apparently just to face Chipper Jones less. It's not that pitchers don't control BABIP-it's that pitchers barely differ in their abilities to control it, because the only control they have is to try and stay unpredictable.

Matt Swartz is an author of Baseball Prospectus. Click here to see Matt's other articles.
You can contact Matt by clicking here

This is one of this week's better ones for me. Two things really stood out. The first was the graphs. I hate graphs for graphs sake, but those really told a story with the straight line contrasting the random scatter plot. The second thing is your last paragraph, which was fantastic.

May 24, 2009 00:16 AM

Will Carroll

BP staff

Great at the beginning, GREAT at the end, but the middle? If I was the Randy of this competition, I'd say "pitchy, dawg." His writing and imagery really resonated and pulled me into the concept, just what should be happening, but the charts killed that flow and didn't have nearly the clarity. Again, this would be something that would have been fixed in the edits, but for the purposes of the contest, I think it's solid enough to get through, but well below what he can do. That's not to say he's not good. He's really good.

May 24, 2009 08:15 AM

Christina Kahrl

BP staff

I really liked how Matt brought in formulas, explained advanced mathematical principles--which again, to harp on this being a "Basics" bit--is critical--and he also effectively delivered information in his charts and tables, and finally, he brought it back to what this can mean in terms of play on the field. If there's one thing I like to see more of, it's the active and effective use of real-world scenarios to convey the fundamental lesson from a statistical argument. Matt did this really very well, with a confidence in his prose that speaks well as far as what we might anticipate his subsequent work.

I liked everything up to and including the graphs and the conclusion. I thought the rock-paper-scissors was an apt way to show game theory and how it applies to pitching theory without pages worth of explanation.

The middle just felt weird to me. My understanding is that while BABIP is not useful for predicting a pitcher's future success per se, it is useful to show how much of a pitcher's performance was affected by luck. So thus, looking at BABIP is useful to see if a pitcher's performance is repeatable.

I also thought the paragraph starting with "If you try to explain this concept to the average baseball fan..." was basically the same thought rerepeated 10 times.

Richard Bergstrom’s comments almost always coincide with what I was thinking including a wait-a-minute moment about disregarding BABIP when projecting a pitcher’s performance. However, Matt is correct if you approach the projection as he described: ignoring ERA in the first place.

Thanks. Both comments are right, just two sides of the same coin. In general, building ERA from components can be better because you can remove two other forms of luck: (1) HR/Flyball luck, which pitchers don't control much, and (2) Strand rate/distribution of runners. A pitcher who gets average BABIP luck but gets all the bloop hits in a row will have an unluckily high ERA that will be likely to improve the next season.

But Richard is correct that removing BABIP luck is certainly a good way to improve ERA projection.

Have to agree with this, in part. The opening sentence of this article is simply awful. I simply cannot imagine how anyone would allow a sentence THAT poorly written to make it into a final draft, let alone allow it to be the opening sentence!

That being said, I thought thing picked up considerably after that, and agree with others that the graphs were particularly enlightening, especially to the "novice" reader.

Overall, I think Matt does a fine job of explaining a rather difficult concept for the average fan.

I have to completely disagree with KG - I really liked the description and analysis of the first 80% of the article, but the last 2 paragraphs are a disaster. Obviously, mixed strategies are likely important to pitchers. But do they explain the low year-to-year correlation in BABIP? I have no idea and there's no reason to believe so based on the statistics presented in this article. The last paragraph is just presented as "I outlined the history of a weird finding and here's a random theory that I will claim explains it."

The author says, "It's not that pitchers don't control BABIP—it's that pitchers barely differ in their abilities to control it..." This is completely wrong. Simply because there's a weak correlation between two numbers does not mean that pitchers have little control. The measure in question could just have a lot of noise so the correlations naturally weaken. There are tons of possible reasons for this noise and a throwing in a game theory explanation isn't really necessary or helpful.

Anyway, I sincerely liked the first part of this article. Stopping before the last paragraph, I would consider it the top entry.

I believe, since McCracken's early exploration of BABIP, the common thought is that pitchers have about a 20% control over BABIP. The trick is that the average pitcher with average BB/9, K/9 and HR/9 home run rates has little overall effect on it, and it's the extreme outliers like knuckleball pitchers or Maddux-esque types who can outguess hitters that account for the variance.

Thus, for most pitchers, it becomes a rock-paper-scissors game of avoiding pitching patterns while also avoiding the chance that the hitter makes a lucky guess on what the pitcher will throw. The better pitchers are one who can vary their pattern the most, or in the case of those like knuckleballers, don't know what their pitch will end up doing in the first place.

I see what you're saying when you say there could just be noise in BABIP, and it's not necessarily something just something pitchers control a little. The issue is what you mean by "a little." Instead, it's probably better to say "Given the large amount of noise in BABIP, the quantitative difference among pitchers in their true BABIPs is small, maybe within .010 of each other for the vast majority of pitchers" I think we probably both agree with that statement?

As far as my mixed strategy theory, I don't really find it so far fetched:

(1) Line drive BABIP is about .730, flyball BABIP is about .140, and groundball BABIP is about .240, and line drives makes up 20% of balls in play-- meaning that line drives are where a large portion of hits on balls in play come from.

(2) Line drive rate has a correlation of actually 0.00 among my sample of 494 pitchers who have thrown 100 innings or more in consecutive seasons from 2003-2008 (retrosheet changed its definition of line drives starting in 2003), meaning that in practice, pitchers really don't seem to be able to affect line drive rate by even the slightest amount at all.

(3) You need to objectively ask yourself what the line drive rate of a pitcher with average skills would be if threw the same pitch in the same location every time. Imagine a fastball on the outside corner. If every hitter knew that was coming, would they still only hit line drives 20% of the time? I can't imagine that anybody believes this. If not, then given (1) and (2), it does seem that preventing line drives is done by playing a mixed strategy so the hitter can't predict what's coming. Hence, the small correlation in BABIP is due to similar abilities in preventing those .730 BABIP line drives.

I'm not positive where your .010 number is coming from but that's likely my fault. I do think that a 0.149 correlation is actually pretty high for a measure that noisy. For one, it just seems like a noisy measure on its own. Second, the denominator is smaller than the other measures (fewer observations->higher variance). And you're subtracting out the team average (which is measured with error itself). This is all fine on your part, but it just means we end up with a noisy measure and a low observed correlation.

I'm sure that mixed strategies are important, but your conclusion doesn't really make sense. If a pitcher threw the same pitch in the same location, the correlation for strikeouts and homeruns would also be pretty high. And they are. The correlation for BABIP would probably be smaller because there would be lots of noise. This is exactly what we see.

So, the evidence doesn't really make your point. I do assume that all pitchers use a mixed strategy (though an article about this using pitch type/location data would be really interesting). But your argument seems to be: pitchers want to prevent line drives, there's no correlation in line drive rates for consecutive seasons -> pitchers used a mixed strategy? Not sure how we reach that conclusion. Are you saying all pitchers used a mixed strategy and mixed strategies lead to randomness? If so, why the correlation in homerun rates (which pitchers are also definitely trying to prevent)?

I'm just missing the link. Are all pitchers using a mixed strategy? Equally well? And then mixed strategies lead to randomness? Or they just lead to better outcomes in general?

The .010 number is basically saying that the vast majority of pitchers' true BABIP ability is probably between .300+/-.010. Sorry about that.

Could you explain what you mean about the denominator being smaller for BABIP? It seems like I'm using the same number of pitchers and there are actually more balls in play than anything else. Do you mean that K/PA has a bigger denominator than Hits/(Balls in Play)? I think that will have some effect but not drastically, but if you mean something else, please let me know.

Of course doing a strategy of throwing the same pitch in the same place repeatedly would also make a pitcher to strike out fewer and give up more homeruns. But when you see a hitter swing and miss at a 100mph fastball, that's pretty clearly something that only certain pitchers can do. When you see a hitter line a single the opposite way on an outside fastball, it's usually about the hitter guessing right or the pitcher making a bad/predictable decision that he learns from and adjusts next time around.

I guess I would say that mixed strategies clearly improve performance, and since everyone does them, the true difference in pitchers seem to be an ability to avoid contact, an ability to find the strike zone, and an ability to cause pitches to arrive at different trajectories and therefore be hit on the ground or in the air.

If a pitcher threw the same pitch in the same location every time, then all that would matter would be #1 the batter's ability to hit that pitch and #2 the batter's ability to hit that pitch for power. The quality of the pitcher's "stuff" (movement, velocity, etc.) would affect #2 since the stuff affects the batter's ability to hit the ball squarely.. but as they say, any major leaguer can hit a straight 100 mph if they know it's coming... though there would still be some effect on #1

So by varying location and pitches, that in effect applies game theory to tilt the odds more in the pitcher's favor because a batter has to react not only to the pitcher's stuff, but the batter is also uncertain of what kind of pitch will be received and what the location of that pitch would be.

Not sure if you're a poker whiz or not, but I keep thinking of Sklansky's Theory of Poker when considering this concept. The idea is that if the batter knew what pitch was coming and where it was thrown, he would perform optimally by picking the correct swing timing, etc. The pitcher's job, then, is to remove the batter's certainty through varying pitch type and location so that the batter handles each pitch in a suboptimal fashion.

.010: Totally not your fault. Not sure I'd agree with the final number, but I'll side with you since you know the literature better.

Denominator: No, you interpreted correctly (PA vs. BIP). Not the main influence, but it probably has some effect.

Mixed strategies: I just don't see how mixed strategies explain the low correlation in BABIP. A few posts below you talk about what would happen if Eck had always thrown the same pitch on 3-2 counts. Yeah, he'd probably be a bad pitcher. But what does this have to do with BABIP?

Are you trying to explain the low line drive rate overall or the low correlation in BABIP? If all pitchers are using mixed strategies equally well, then it's probably not the reason for the low correlation.

Here's an analogy: What would happen if Eck could only throw 55 MPH? His line drive rate would probably skyrocket. Since his line drive rate was low, I can assume he threw more than 55 MPH. I guess I can assume every pitcher throws more than 55 MPH because of the low line drive rate in the majors. But that doesn't mean I can say anything about it explaining BABIP correlations. I get that mixed strategies are good for pitchers' performance stats. They have to be. But why does this affect BABIP correlation? There's a major link missing here in your argument.

I'll give you a sense of where I'm getting the .010 number. The standard deviation of what I call "net BABIP" (which is just the pitcher's BABIP minus his whole pitching staff's BABIP that year) is about .020, but that is a mixture of noise and a tiny bit of skill. I tried to run a regression analysis to predict net BABIP as a function of net BABIP the previous year and two variables that are clearly relevant pitcher skills and which both have small but significant effects on net BABIP-- strikeout rate (strikeout pitchers have lower BABIPs) and groundball rate (groundball pitchers have higher BABIPs). Then I use the regression formula to get a predicted net BABIP for the second year as a function of net BABIP the first year, strikeout rate the first year, and groundball rate the first year. The result is a predicted net BABIP variable with a standard deviation of about .0045. I'm sure not everyone has the statistics background to follow that general description, but basically it's a pretty solid way to get an idea of the standard deviation in BABIP skill. If the standard deviation is about .0045, then about 95% of players would fall +/- .009 of average BABIP of their team with infinite data. I rounded up to .010 since I assume there are probably other small factors at play other than the ones I included.

The reason that mixed strategies have to do with BABIP is that the typical counterargument to the statement that pitchers don't really control BABIP is "if that were true than there is nothing a pitcher could do to have a high BABIP, but we know that's not true." I am explaining away that hole in the argument so that it's clearer to people. It's true that if Eck threw 55 MPH or the average high school pitcher went out there and did the same, BABIPs would be high, but among major league pitchers, there aren't pitchers like that. I've read that minor league pitchers with very high BABIPs are likely to fail, since they are too far from major league level but the skill distribution at the top in terms of BABIP is very small. Not playing mixed strategies would make them terrible, but when they are playing smart mixed strategies, the only real difference is missing bats (leading to Ks), missing the strike zone (leading to BBs), or throwing pitches that have a trajectory such that they will be hit in the air more (leading to flyballs, and hence, HRs). Line drives really do seem to be a product of good guesses and quick hands of the hitter.

I'm trying to explain the low line drive rate actually. The reason I do that is because we know that pitchers differ in their groundball/flyball ratio and that any team is bound to have a higher BABIP on groundballs than flyballs. Picking through the data, I found that the reason that the correlation was so small anyway was the inability of pitchers to prevent line drives was the same across players, and that line drives make up a very large share of hits on balls in play anyway. That's why the correlation is low-- the key component has no year to year correlation.

1) I don't think it's good practice to use the predicted BABIP to get a standard deviation measure of true ability. For any regression, you have explain and unexplained variance. You've only used the explained variance but since you have a poor (noisy) measure of true ability, this doesn't really give you the right number (eg. regress wages on IQ and get predicted wages -> you wouldn't claim the std dev for the predicted wages is the correct std dev for wages since there's so much unexplained ability). Anyway, this actually isn't that big of a deal to me.

2) At the risk of coming off snarky, your theory is completely wrong (or at least illogical). You're just saying mixed strategies lead to better performance. So does throwing harder than 55MPH. Your point seems to be - all pitchers use mixed strategies (equally well?) and mixed strategies lead to randomness. By your logic, we should see no correlation in HR rates as well. It would be incredibly interesting (though, I'm guessing, difficult) if you did a study on pitch types/location to study patterns, looking at whether predictability affects BABIP. But you just can't prove it with the stats you've presented.

The explanation for this theory really has to center around why HR rates are highly-correlated across years, but BABIP is not. The general answer is noise. Take a HR ball and move it over 1 foot - probably still a HR. Now take a grounder and move it over one foot - the probability that the outcome changes (out->hit) is higher. Or don't move it over at all and the probability of an outcome change is still non-zero (fielder is a step slow in one scenario). This "extra randomness" just means more noise. Noisy measures are less-correlated.

Anyway, I really do appreciate the responses and I did vote for you (one of 2).

Thanks for your vote. I enjoy your criticisms since you clearly know what you're talking about, and I can learn about things and challenge my theories.

1) The point about using a regression to find SD is a fair criticism. I guess that only works if you assume I have found the true function using the regression, which only is a good argument if you agreed with me in the first place. Tell me if this one helps (I have a feeling you will have no problem following my math but tell me if I'm leaving something out, and I apologize to any other reader who I'm skipping steps with)...
a) netbabip has a standard deviation of about .0202.
b) the average pitcher in my sample has about 560 balls in play and a .297 BABIP meaning that if there were no skill in BABIP at all, you would get a standard deviation of around sqrt(.297*.703/560)=.0193 (using the binomial distribution).
c) The variance in ability + the variance due to the binomial distribution should add up to the total variance in the sample. meaning that the true standard deviation, SD, should be such that SD^2 + .0193^2 = .0202^2. That gets me SD=.006 which is close to the .005 I got using the regression.
...Does that make sense? I recognize your disagreement on the log wage vs IQ regression example, so I thought this might be another way.

2) I still think my theory is right, but what you say is also true about the one foot difference in HR vs GB. There are a few things that are true...one is that mixed strategies will improve pitching performance, and that everyone using mixed strategies will lead to a smaller distribution in performance than some people mixing and some being predictable. I think we already agreed on that point. The other is that the more variance in the skill level involved in a certain statistic among MLB pitchers, the more variance in performance.

The variance in HR/9 comes primarily from variance in flyball rate. There is not much variation to HR/Flyball. So the variance in angles that a pitcher's pitches reach the batter is high enough that flyball rate is highly varied even though all pitchers are mixing strategies (though not as much as if pitchers additionally varied between smart mixed strategies and dumb pure strategies).

The variance among major league pitchers in throwing pitches that the batter can square up and hit a line drive against when they guess right (and the variance among major league pitchers in throwing pitches that the batter can square up and hit a line drive against when they guess wrong) are both not so highly varied so the primary variance in line drive rate comes from randomness in how often hitters guess right.

In general I'm saying that mixed strategies help with GB/FB ratio, K rate, BB rate AND LD rate, BUT there is still a large variance in GB/FB, K, and BB skills among major league pitchers, and LD rate does not have much variance in skill level among major league pitchers-- as long as they play a mixed strategy...which they do. The key is that LD rate would have persistence if some pitchers mixed strategies and others didn't.

I would have loved to see the last paragraph expanded into half the article, as it explains WHY pitchers show little difference in BABIP skill compared to hitters, after spending the first half getting readers to see that it's actually true. Still one of my favorites.

I agree with your comment about expanding the last paragraph, except in the context of this articles requirements, where further discussion of the concepts in the last paragraph probably would've been "Beyond the Basics".

I agree this rolled along nicely for the most part. For me it started to get bogged down and repetitive in the paragraph beginning "To project future hitting . . .", then it ended nicely. Although, I found Matt's original entry one of the weaker ones (the prisinor's dilema), this makes enough ground for me to endorse his continuance to the next round.

I liked this one. Matt took something that is generally discussed (BABIP) and showed the reader valuable correlations. The graphs were a great touch. Its very easy to see the difference in the correlations.

Second, the rock-paper-scissors part might have been more compelling if it started with the premise that the goal is to prevent line drives.

Finally, further analysis could look at whether some group of pitchers *do* seem to have a year to year positive correlation in line drive rate to see what we could learn (Johan Santana or Roy Halladay, perhaps?).

Agreed-- it might have been nice to include the line drive graph, too. It would look very similar to the net BABIP graph and since I was technically describing BABIP, I was afraid it would be overkill to do another graph with line drives. Looking through the comments, it seems that the line drive thing interested people as much as I was hoping and I didn't need to hedge as much and could have included that.

You make a good point about linking the rock/paper/scissors to the goal of line drive prevention earlier.

It's tough to know about individual pitchers because even if you looked at a statistic that is totally random, there will be consistent outliers who are very unlucky a few times in a row or very lucky a few times in a row. It's easier to look at groups of pitchers. Knuckleballers have a lower BABIP than other pitchers, according to what I've read (though I admit I have not looked into this myself). High strikeout guys tend to also-- so that would explain some of Johan Santana. But that effect is small. The BABIP effect of strikeout rate might explain only a few points of BABIP.

Overall, I thought this was a strong article. The opening was a weak point. I also thought at times it came close to being a bit condescending. I would also avoid using exclamation points where they aren't necessary.

The analysis was very strong and did a good job of letting the reader know why the stat is important. Gets my vote.

First, I think he unnecessarily assumed an excluded middle in his opening paragraph, which is a shame, because it was really just flavour-text.

But more than that, he titled the article very badly. This article does an excellent job of showing that pitchers don't control BABIP, and does so by wonderfully illustrating (with great graphs) that year-to-year BABIP is effectively random.

But that's not what the article said it was going to do. The title reads "Why Pitchers Don't Control Batting Average on Balls in Play", and that question not only wasn't answered, but it wasn't even approached.

Demonstrating that something is true (which this article does very well) does not constitute an explanation as to why it is true.

A borderline vote? I guess that gives me quite the incentive to make my case! :-)

I felt that I explained the reason why pitchers have little to no control over BABIP had to do with playing a mixed strategy. Earlier in the article, I isolated that the true non-correlating variable (in fact, 0.00 correlation) was the line drive rate. I explained how mixed strategies are a common choice among pitchers, and why BABIP would vary wildly between pitchers if pitchers made the same predictable pitches. I read on Wikipedia (http://en.wikipedia.org/wiki/1988_World_Series) that Kirk Gibson hit his homerun off Dennis Eckersley in Game 1 of the 1988 World Series precisely because Dodgers' scout Mel Didier had said that Eckersley always threw a backdoor slider to lefthanded power hitters on 3-2 counts. That is not a mixed strategy. What happened? He hit it out of the park! Now, that's not a line drive, but if Eckersley didn't start mixing his strategy in 1989, don't you think that lefthanded power hitters would have started hitting a lot of doubles in the gap on 3-2 backdoor sliders? Sure, but pitchers adjust, and I'm sure Eckersley learned his lesson. The result? No correlation in BABIP from one year to the next.

More to the point, this could just as easily be explained by psychology rather than game theory. If *you* gave up a home run in that big of a situation with that pitch, would you use the same pitch in *every* similar situation the next season? It's a milder baseball equivalent of a post-traumatic stress reaction.

Not to mention every player in MLB saw what pitch Gibson hit out of the park. It would be like a mini training video on how to hit Eck.

This is a very good point, but the two aren't mutually exclusive. Much of the time, people do behave in ways that are similar to what economic theory would predict, even though they aren't necessarily aware why they are doing them. Consider a pool player. By hitting the ball at the speed and angle off the wall just perfectly, he makes a shot. In reality, he's solving a very complicated differential equation-- but he's not actually doing any math consciously. He's just trained himself to behave that way and people who behave that way succeed. If you asked him why he did that, he wouldn't show you a differential equation, but that's effectively what he did. The pitcher or pitching coach might not be aware of the mixed strategy element to what he's doing, but any pitching coach who doesn't tell his pitchers to keep the hitters honest is going to lose his job when they happen to fail a lot.

I thought the explanation of correlation coefficient was very good, especially when backed up by the two graphs showing visually what a .15 correlation looks like compared to a .77. And the "face Chipper Jones less" comment was dead on in highlighting the gap between hitters' consistency in BABIP and that of pitchers.

I would have liked to see more about line drive/flyball/groundball percentages, and how they impact BABIP. Although, possibly that would have led away from the "Basics" format.

I've actually done a ton of research on line drive/flyball/groundball percentages and BABIPs, moreso for hitters than pitchers, and I'll keep in mind that readers (voters!) are looking for that too. A little sneak peak-- line drive rate even for hitters has weak correlation (though it's definitely there); groundball and flyball rates are far more persistent. Line drive BABIP tends to be about .730 on average, though it's higher for power hitters. Groundball BABIP tends to be about .240 on average, though it's better for faster players and better contact hitters (who chop the ball off the bottom of the bat less often). Flyball BABIP tends to about .140 on average, but varies wildly depending largely on infield fly rate. If I move forward in the competition and the topic matches up, I'll do my best to include articles touching on these topics.

Sure. ISO has about a .40 correlation with flyball rate. OBP has a .45 correlation with ISO, but the reason is not patience. It's the pitchers who are doing that. The percent of pitches in the strike zone seen has a -.35 correlation with ISO. The percentage of pitches out of the strike zone swung at is effectively uncorrelated with ISO, but the percentage of pitches in the strike zone swung at has a .26 correlation with ISO. Power hitters capitalize on strikes more rather than laying off balls more. They have higher OBP because pitchers are afraid of them.

Ok, so... some facet of power comes from a batter's ability to patiently wait, and then swing for a pitch in the strike zone. Can it be implied that power hitters tend to hit strikes more squarely/solidly/"with more strength" than non-power hitters? Or perhaps a line drive rate or fly ball rate can be used as an indication?

If players have a higher OBP because pitchers are afraid of them, how does that explain the Alfonso Sorianos of the world? Is it that he has power, but pitchers know how to pitch to him?

I would agree that power hitters hit strikes more solidly than non-power hitters, but I'm not quite sure that they hit them more squarely. The correlation between ISO and LD% is virtually zero if not negative. The correlate between LD-BABIP and ISO is pretty high. They hit their line drives further, I'd say.

There is still a lot of variance in patience among power hitters. Alfonso Soriano swings at 35-40% of pitches out of the strike zone versus a league average of under 25%. He only walks at all because pitchers throw him enough pitches far enough out of the strike zone that he lays off them now and then.

Oh, I thought you meant them differently. I was using "squarely" to mean putting the center of the barrel on the ball, leading to high line drive rate, and I was using "solidly" to mean hitting the ball harder and further when they do that, leading to fewer line drives being caught.

I don't think power comes from patience. I think that you need to have some mixture of power and patience to make the majors, and the correlation in walk rate and homerun rate is due to pitchers throwing fewer strikes. There is no correlation between power and laying off bad pitches, but there is a correlation between power and swinging at good pitches. Soriano is powerful in general and may be more powerful if he had a better eye. It's tough to separate having a good eye from being patient. I'm pretty sure Soriano doesn't really have either quality (eye/patience) but has enough power to keep hitting homeruns anyway. A hitter can make the majors with a poor eye only if he has enough power or contact skill to make up for it. Juan Pierre with Soriano's eye and patience would never have made the majors with Juan Pierre's power deficiencies and Soriano's eye.

When you mention a mixture of power and patience, I'm almost wondering if it's a supply/demand problem... the supply being the kinds of pitches a pitcher throws versus the demand of the kinds of pitches a hitter will swing at. Probably outside the scope of the discussion of this article, but might be worth some additional thought...

one question i've always had about babip was why it includes foul outs.. because it's easier that way or because it gives us better information to include them? to me, it seems that a player who plays in a home park with expansive foul territory, or just tends to foul off a higher percentage of pitches in general and therefor pops out in foul territory more, will have an artificially low babip because if those balls "in play" weren't converted for outs, they wouldn't be hits either. so it assumes that two players with equal babips (and for the sake of argument, equal line drive %, speed, etc.) but an unequal # of foul outs have been equally lucky/unlucky.

since you've made your case in exchange for a vote to other readers in these comments, i'd love to hear some thoughts or analysis on this in turn for my vote..

You're absolutely right that foul outs affect BABIP a lot. They are also highly dependent on park factors-- stadiums with huge foul ground obviously lead to more of them. In reality, there are other park factors that influence BABIP too. The more outfield ground to cover, the higher BABIPs will be at that stadium. Ultimately, that's part of the goal in looking at net BABIP (individual pitcher's BABIP minus his team's pitching staff's BABIP)-- to remove those effects.

At the same time, it's better to include them because they are a skill. Hitters especially have a lot of control over their infield fly rate, and infield fly rate is naturally correlated with foul fly rate. So it makes sense to include avoiding or recording foul outs as a skill, albeit while understanding that park effects play a large role.

Great article Matt. On a reader's level, you do a better job than most explaining as you give information. It's a task that most teachers have to deal with. On sites like this, there is a fine line between giving enough information and too much (where people talk down to you).

Good information as well. I was hoping that there would be a big string at the end to draw everything together. If a pitcher has to try to be more random, are some pitchers better at it? Why some years are they more random than others? I think the key component to that is the catcher. I think adding the catcher in as a variable is almost as important as HRs, BBs or Ks.

Good point-- the catcher probably does play a pretty big role (and I guess probably the pitching coach or manager plays a role if either is calling the pitches too). That type of stuff presumably gets negated when looking at net BABIP (individual pitcher's BABIP minus his whole team's pitching staff's BABIP), since the catcher presumably catches all of the pitchers equally (or close to it). I guess I'm not sure that too many pitchers themselves are better at randomizing their pitches or at least if they are, they probably are able to transfer this skill to teammates.

OK here's another angle... a fast runner on first base makes a pitcher more likely to throw fast pitches (so the catcher has a chance at a caught stealing). A runner on third base also makes a pitcher more likely to stay away from breaking balls and other balls in the dirt (that might allow a wild pitch/passed ball). Is there a correlation with BABIP and runners on first base or on third base?

"For pitchers, the best way to have a low BABIP is apparently just to face Chipper Jones less."

...and yet you didn't really address quality of opposition faced, or how much it might affect year-to-year correlation of BABIP.

Pretty good overall. I'll agree with the people who have called the first sentence "a disaster", and also those who found it disingenuous to present your discussion of randomized strategies as an *explanation* of the low year-to-year correlation in BABIP. Hint: why doesn't the same explanation lead to low HR/9 correlation?

It's very true that facing the same hitters the following year is playing a role. That's a good point, and one of the reasons that I used "net BABIP" (individual pitcher's BABIP minus his team's pitching staff's BABIP)-- since his teammates face the same guys. That is an excellent point though, and something I did not highlight and perhaps should have.

I discussed the HR/9 thing a little in other comments, but the real reason is fascinating. The reason that pitchers have higher correlation with respect to HR/9 is that those who give up a lot of homeruns are flyball pitchers. The rate of homeruns/flyball has very little correlation year to year. There certainly is a high correlation in flyball rate, since pitchers throw different pitches that come in at different angles. That's where most of the variance in HR/9 comes in. I always wondered that about BABIP too, and the HR/Flyball issue resolved it for me a good deal.

1. The other pitchers on a team do NOT face the same hitters, especially under the anti-balanced schedule with interleague play. The differences are significant, and BP reports them in a canned report on the Statistics page. This deserved at least an aside.

2. The problem is not whether it is true that HR/9 is pitcher independent and BABIP is not. The problem is that the argument/explanation you gave for BABIP would apply equally to HR/9, and so can't be right -- or at least not sufficient. That's a flaw of logic and exposition, not of fact.

1) Of course pitchers on the same don't face the same hitters in any given year. But a guy who faced the Yankees a lot one year is no more likely than other pitchers on his team to face the Yankees a lot the next year. So I'm not sure that matters. Do you disagree with that? Do teams consistently always throw the same pitcher against the same opponents? Keep in mind that my data set is pitchers with over 100 innings so this should be starters only.

2) My goal is to explain the lack of correlation in line drives. Flyball rate clearly has a higher correlation, and this is why HR/9 has a higher correlation. HR/Flyball (adjusting for park) does not have a high correlation at all. The theory I'm using works fine if you assume that homeruns are not "correct guesses" much more than other flyballs are. Line drives tend to be a product of good guesses. Homeruns are flyballs hit by more powerful hitters.

If this theory were legitimate you could rank pitchers by somme ratio of strikeout rate to homerun rate. And you can't. Some pitchers can pitch where hitters will pop it up others pitch to hit grounders (which have a less of a chance of becoming hits than do line drives) and other pitchers pitch to the defense.
For all of you statisticians out there, there is a whole wide world of pitching formulae, ratios and analytical tools for you to develop.
Bogus

There are formula like the ones that you mention. FIP is one developed by Tom Tango, QERA is developed by Nate Silver. Those two are great. I did not invent this theory (I wish I did)-- Voros McCracken, current Red Sox employee did.

I understand that the conventional wisdom says that pitchers can make hitters pop it up or avoid hitting line drives but the fact is that were true, the same pitchers who succeeded in one respect would be more likely to the following year. With line drive rate, it's simply not true. Groundball pitchers actually have higher BABIPs since flyballs have lower BABIP than groundballs.

I think you are overgeneralizing here. Some pitchers have BABIP that fluctuate but others have consistently low (or high) BABIP. So, there must be something about these kinds of pitchers that cause them to be outliers.

Btw, the statement "Groundball pitchers actually have higher BABIPs since flyballs have lower BABIP than groundballs." is worded awkwardly at best and is iffy kind of logic at worst... you are basically submitting a proof by surmising its opposite is true but not providing proof of that opposite. I realize that there are stats that flyball pitchers get more popouts, so have a lower BABIP, while groundball pitchers are more likely to give up hits because balls can sneak through fielders, but the statement as you word it does not say that..

groundballs sneak past the defense more often than flies. groundball pitchers, by definition, generate more groundballs than flyball pitchers. it's a generalization, but reasonable enough to conclude that groundball pitchers will have higher babips...

Yep, this is the kind of statistics table I meant and I believe I saw something similar to this either here on BP or in one of the BP Annuals.. I was just saying that without knowledge of this table, people might question the statement you made.

If this theory is true, then why isn't the inverse true? Not only are strikeout rates not considered that relevant for hitters (except in, say, projecting whether or not a minor leaguer can compete at the next level) but are often scoffed at.
I would also point out that if you're really talking about "missing bats" you should exclude called third strikes. After all, many good strikeout hitters get their strikeouts by hitting corners. And if the hitter reaches for these balls, instead of taking it, he's likely to hit it weakly; purpose achieved either way.

This week's assignment was really a no-win proposition for the contestants: write something for a general, non-BP audience that will be read and judged by serious BP insiders. This was also one of the last papers I read, so I came to it having already read 3 or 4 papers that covered interrelated BABIP/DER territory with similar prose.

That said, over two rounds of submission Matt has proven himself one of the best at structuring his articles, builing a sound organization around his thesis, executing with solid prose, and connecting the numbers to real-world examples. I especially found his original piece creative and engaging, and I hope future rounds give him greater opportunity to display that potential.

I think the comments section is the "Coke chair" of BP Idol. Some authors have been extremely engaging and have been able to show their stuff well beyond what they showed in the article. Great strategy...

I agree that I think commentary is important. I hadn't based my votes yet on commentary, but if it gets to the later stages, I can see commentary being a deciding factor. I think it is important for a finalist (or any kind of writer) to show they are reading and evaluating their feedback, even if they don't agree with it. Collaborative learning at its finest!